Figures - Standard CP plate and the Phalloidin400LS¶

Data were negcon normalized

In [ ]:
### Modules import
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.io as pio
import pandas as pd

mAP calculated as difference to controls¶

Reading the dataframe¶
In [ ]:
combined_moa_cellcount_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_cellcount_Negcon_wrt_Controls.csv')
combined_moa_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_Negcon_wrt_Controls.csv')

Comparison of Mean average precision¶

Standard cellpainting data vs Phalloidin 400LS¶
In [ ]:
actin_fig = px.scatter(combined_moa_cellcount_df, x =combined_moa_cellcount_df['average_precision_std'], y=combined_moa_cellcount_df['average_precision_act'],labels={'average_precision_std':'Mean Average Precision - Standard CellPainting dyes', 'average_precision_act':'Mean Average Preicison - <br> Phalloidin 400LS (long-stoke shifted)'}, color=combined_moa_cellcount_df['MoA'])
actin_fig.update_layout(legend=dict(orientation="h"), height=800, width=1000)
actin_fig.show('notebook')

Mean average precision values of all compounds¶

In [ ]:
combined_box_plot = go.Figure()

combined_box_plot.add_trace(go.Box(y=combined_moa_df['average_precision_std'], name = 'Standard Cellpainting dyes', boxpoints='all', hovertext=combined_moa_df['MoA']+'-'+ combined_moa_df['Common Name']))
combined_box_plot.add_trace(go.Box(y=combined_moa_df['average_precision_act'], name = 'Phalloidin 400LS', boxpoints='all', hovertext=combined_moa_df['MoA']+'-'+ combined_moa_df['Common Name']))
combined_box_plot.update_layout(height=800,width=1000, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision')
combined_box_plot.show('notebook')

Mean average precision values - MoA¶

The size of the markers represent the average number of cells present in the replicates. The number of cells were normalized by dividing the actual number by 100 for easier plotting.

In [ ]:
scatter_plot = go.Figure()
scatter_plot.add_trace(go.Scatter(x=combined_moa_cellcount_df['MoA'], y=combined_moa_cellcount_df['average_precision_std'],hovertext=[combined_moa_cellcount_df['Metadata_Count_Cells_Std']], mode='markers', name = 'Standard Cellpainting dyes', marker_size =combined_moa_cellcount_df['Metadata_Count_Cells_Std_norm'] ))
scatter_plot.add_trace(go.Scatter(x=combined_moa_cellcount_df['MoA'], y=combined_moa_cellcount_df['average_precision_act'],hovertext=[combined_moa_cellcount_df['Metadata_Count_Cells_act']], mode='markers', name = 'Phalloidin 400LS', marker_size =combined_moa_cellcount_df['Metadata_Count_Cells_act_norm']))
scatter_plot.update_layout(height=1000,width=1500, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision',  legend=dict(yanchor="top",y=0.99,xanchor="left",x=0.01))
scatter_plot.update_xaxes(tickangle=90, categoryorder='total ascending')
scatter_plot.show('notebook')

Difference in mean average precision values¶

The negative values indicate the better performance of Phalloidin 400LS

In [ ]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=combined_moa_df['MoA'], y=combined_moa_df['std_vs_act'],mode='markers', hovertext=combined_moa_df['Common Name']))
fig.update_layout(height=1000,width=1700, font_family='Arial', font=dict(size=14, color='Black'))
fig.update_yaxes(title='Difference in <br> Mean average precision')
fig.update_xaxes(categoryorder='total ascending')
fig.show('notebook')

mAP calculated as difference to other treatments¶

Reading the dataframes¶

In [ ]:
combined_moa_cellcount_trmt_df = pd.read_csv('copairs_csv\\PrecisionValues_with_MoA_allplates_cellcount_Negcon_wrt_trmt.csv')

Comparison of Mean average precision¶

Standard cellpainting data vs Phalloidin 400LS¶
In [ ]:
actin_fig_1 = px.scatter(combined_moa_cellcount_trmt_df, x =combined_moa_cellcount_trmt_df['average_precision_std'], y=combined_moa_cellcount_trmt_df['average_precision_act'],labels={'average_precision_std':'Mean Average Precision - Standard CellPainting dyes', 'average_precision_act':'Mean Average Preicison - <br> Phalloidin 400LS (long-stoke shifted)'}, color=combined_moa_cellcount_trmt_df['MoA'])
actin_fig_1.update_layout(legend=dict(orientation="h"), height=800, width=1000)
actin_fig_1.show('notebook')

Mean average precision values of all compounds¶

In [ ]:
combined_box_plot_1 = go.Figure()

combined_box_plot_1.add_trace(go.Box(y=combined_moa_cellcount_trmt_df['average_precision_std'], name = 'Standard Cellpainting dyes', boxpoints='all', hovertext=combined_moa_cellcount_trmt_df['MoA']+'-'+ combined_moa_cellcount_trmt_df['Common Name']))
combined_box_plot_1.add_trace(go.Box(y=combined_moa_cellcount_trmt_df['average_precision_act'], name = 'Phalloidin 400LS', boxpoints='all', hovertext=combined_moa_cellcount_trmt_df['MoA']+'-'+ combined_moa_cellcount_trmt_df['Common Name']))
combined_box_plot_1.update_layout(height=800,width=1000, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision')
combined_box_plot_1.show('notebook')

Mean average precision values - MoA¶

The size of the markers represent the average number of cells present in the replicates. The number of cells were normalized by dividing the actual number by 100 for easier plotting.

In [ ]:
scatter_plot_1 = go.Figure()
scatter_plot_1.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['average_precision_std'],hovertext=combined_moa_cellcount_trmt_df['Common Name'], mode='markers', name = 'Standard Cellpainting dyes', marker_size=combined_moa_cellcount_trmt_df['Metadata_Count_Cells_Std_norm']))
scatter_plot_1.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['average_precision_act'],hovertext=combined_moa_cellcount_trmt_df['Common Name'], mode='markers', name = 'Phalloidin 400LS', marker_size=combined_moa_cellcount_trmt_df['Metadata_Count_Cells_act_norm']))
scatter_plot_1.update_layout(height=1000,width=1500, font_family='Arial', font=dict(size=14, color='Black'), boxmode='group',yaxis_title = 'Mean average precision',  legend=dict(yanchor="top",y=0.99,xanchor="left",x=0.01))
scatter_plot_1.update_xaxes(tickangle=90, categoryorder='total ascending')
scatter_plot_1.show('notebook')

Difference in mean average precision values¶

The negative values indicate the better performance of Phalloidin 400LS

In [ ]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=combined_moa_cellcount_trmt_df['MoA'], y=combined_moa_cellcount_trmt_df['std_vs_act'],mode='markers', hovertext=combined_moa_cellcount_trmt_df['Common Name']))
fig.update_layout(height=1000,width=1700, font_family='Arial', font=dict(size=14, color='Black'))
fig.update_yaxes(title='Difference in <br> Mean average precision')
fig.update_xaxes(categoryorder='total ascending')
fig.show('notebook')